Research Paper on the Impact of Age Demographics on Voting Patterns in the 2022 Philippine Presidential Election Using Correspondence Analysis

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Introducción y muestreo

Fonte DataSUS

My homework assignment for DSCI 101, Spring 2025.

A quick introduction about me.

--- title: "Introduction to Time Series Analysis: Simulating and Visualizing Data" author: "Nishanth Seeniasakap Perumal" date: "`r Sys.Date()`" output: html_document: theme: cosmo highlight: tango toc: true toc_float: true code_folding: show --- ## Introduction This document provides an introduction to **time series analysis** using R. We will: 1. Simulate a time series using the `runif()` function. 2. Convert the data into a quarterly time series object. 3. Visualize the data using static and interactive plots. 4. Apply a **6-period rolling average** to smooth the time series. 5. Create an interactive **dygraph** for exploratory analysis. All code and outputs are included in this document. You can interact with the graphs directly in the browser! --- ## Simulating the Time Series We start by simulating a time series with 99 data points. The values are randomly generated between 10 and 45 using the `runif()` function. ```{r simulate, echo=TRUE, message=FALSE, warning=FALSE} # Set seed for reproducibility set.seed(123) # Simulate 99 random points between 10 and 45 mytsdata <- runif(n = 99, min = 10, max = 45) # Convert to a quarterly time series starting in 2000 myts <- ts(mytsdata, start = c(2000, 1), frequency = 4) # Display the first 10 values head(myts, 10)

Lab #2